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Predicting lattice thermal conductivity via machine learning: a mini review
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Journal Article
Predicting lattice thermal conductivity via machine learning: a mini review
2023
Overview
Over the past few decades, molecular dynamics simulations and first-principles calculations have become two major approaches to predict the lattice thermal conductivity (
κ
L
), which are however limited by insufficient accuracy and high computational cost, respectively. To overcome such inherent disadvantages, machine learning (ML) has been successfully used to accurately predict
κ
L
in a high-throughput style. In this review, we give some introductions of recent ML works on the direct and indirect prediction of
κ
L
, where the derivations and applications of data-driven models are discussed in details. A brief summary of current works and future perspectives are given in the end.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
